摘要
针对积累矩阵在低信噪比、低信杂比目标检测时存在的峰值簇拥现象,提出了一种有效的Hough变换积累方法。该方法综合利用单元投票数和投票取值定义了反映单元对应参数为待测曲线特征参数可能性的置信因子,并依据置信因子衡量各投票点对相应单元的贡献值,从而对参数空间实现加权积累。不同量测噪声水平以及不同杂波水平下的多目标航迹起始仿真表明,该方法不但能够明显改善局部峰值簇拥现象;而且对量测噪声和杂波干扰都具有更好的鲁棒性,特别适用于复杂环境中的目标检测。
To solve the problem of ＂peak clustering＂ in Hough Transform-based target detection, an effective accumulation method was proposed. Its main idea is that it is more likely for an estimated parameter corresponding to the cell with narrower sample dispersion and more vote-points to be the actual parameter. In the proposed method, the confidence factor of the cell, which represents the probability of the corresponding parameter to be the actual one, is defined first using the vote number of the cell and the transformed values of its votes and its neighbors＇ simultaneously; then the contributions of each cell＇s votes are determined based on the confidence factor of that cell and its neighbors. Simulations of multitarget track initiation with varied measurement noise and varied clutter densities are performed. The results show that compared with binary accumulation the method alleviates the problem of ＂peak clustering＂ and makes significant improvement in reducing false tracks. Besides, the proposed method has stronger robustness to measurement noise and clutters, especially in complex environment.
出处
《系统仿真学报》
EI
CAS
CSCD
北大核心
2007年第4期811-814,共4页
Journal of System Simulation
基金
国家自然科学基金(60404011
60372085)
关键词
HOUGH变换
峰值簇拥
权值积累
航迹起始
多目标跟踪
Hough Transform
peak clustering
weight accumulation
track initiation
multiple target tracking
